Generalized linear models for symbolic polygonal data.

Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using symbolic polygonal data, this paper introduces a linear regression approach for polygonal data based on the generalize linear model theory that provides a unified method to broad range of modeling problems for different types of response as asymmetric continuous and discrete. Ordinary polygonal residuals and a way for finding model inadequacies are presented. Moreover, a quality measure of fit for polygons is also proposed in this paper. Experimental evaluation results illustrate the usefulness of the proposed approach regarding synthetic and real polygonal data. • An approach based on Generalized Linear Models for symbolic polygonal data is proposed. • Polygonal residuals are defined for evaluating the adequacy of the fitted model. • The prediction quality is measured by a metric based on Euclidean distance and polygon vertices. • Synthetic and real polygonal data sets are considered in the experimental evaluation. [ABSTRACT FROM AUTHOR]